Superpixel Guided Network for Weakly Supervised Semantic Segmentation

Abstract

Image-level weakly supervised semantic segmentation faces challenges in accurately capturing boundaries and representing intricate details due to the absence of pixel-level supervision. Constrained by the enormous number of pixels, pixel-level propagation has difficulty in capturing the long-range dependency, particularly in small, isolated regions. To this end, we introduce a novel approach of self-supervised segmentation integrated with superpixel, and develop a network called superpixel guided network (SPGNet) to simultaneously perform superpixel generation and segmentation mask prediction. Significantly, our framework facilitates mutual supervised learning between the segmentation branch and the superpixel branch. The superpixel guides the predicted mask for improved boundary location, while the latter provides supervision on superpixel through superpixel center generation (SCG) and union boundary extraction (UBE). Furthermore, we propose superpixel context fusion (SCF) to generate compact pseudo masks and capture long-range dependency. Experimental results demonstrate that the proposed SPGNet achieves outstanding performance on the PASCAL VOC 2012 segmentation benchmark